{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A StoDynProg usage example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document contains a step-by-step example on:\n",
    "\n",
    "* how to to use the opensource `StoDynProg` library (https://github.com/pierre-haessig/stodynprog),\n",
    "* in order to solve the *Optimal control of an Energy Storage with an AR(1) input sollicitation*.\n",
    "\n",
    "Pierre Haessig — July 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#System-description\" data-toc-modified-id=\"System-description-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>System description</a></span><ul class=\"toc-item\"><li><span><a href=\"#Dynamics\" data-toc-modified-id=\"Dynamics-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Dynamics</a></span></li><li><span><a href=\"#Set-of-admissible-controls\" data-toc-modified-id=\"Set-of-admissible-controls-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Set of admissible controls</a></span></li><li><span><a href=\"#Cost\" data-toc-modified-id=\"Cost-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Cost</a></span></li><li><span><a href=\"#Collect-all-these-function-in-one-SysDescription-object\" data-toc-modified-id=\"Collect-all-these-function-in-one-SysDescription-object-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Collect all these function in one <code>SysDescription</code> object</a></span></li></ul></li><li><span><a href=\"#Control-optimization-with-SDP\" data-toc-modified-id=\"Control-optimization-with-SDP-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Control optimization with SDP</a></span><ul class=\"toc-item\"><li><span><a href=\"#Create-a-DPSolver-object\" data-toc-modified-id=\"Create-a-DPSolver-object-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Create a <code>DPSolver</code> object</a></span></li><li><span><a href=\"#Policy-optimization-with-&quot;Policy-Iteration&quot;-algorithm\" data-toc-modified-id=\"Policy-optimization-with-&quot;Policy-Iteration&quot;-algorithm-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Policy optimization with \"Policy Iteration\" algorithm</a></span></li></ul></li><li><span><a href=\"#Plot-the-energy-manangement-policy\" data-toc-modified-id=\"Plot-the-energy-manangement-policy-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Plot the energy manangement policy</a></span><ul class=\"toc-item\"><li><span><a href=\"#3D-plot-of-the-policy\" data-toc-modified-id=\"3D-plot-of-the-policy-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>3D plot of the policy</a></span></li><li><span><a href=\"#2D-plot-of-the-policy\" data-toc-modified-id=\"2D-plot-of-the-policy-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>2D plot of the policy</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "#%config InlineBackend.figure_formats = ['svg']\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## System description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem description overview:\n",
    "\n",
    "![Storage management problem](app_stodynprog_files/storage_management_EN.png)\n",
    "\n",
    "Control objective : manage the power flows (e.g. storage power $P_{sto}$) to minimize system cost (e.g. deviation penalty on $P_{dev}$) despite the random perturbation $P_{mis}$.\n",
    "\n",
    "Power conservation equation:\n",
    "\n",
    "$$P_{dev} = P_{mis} - P_{sto} - P_{cur} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters of the problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy storage capacity: 10.0 MWh\n"
     ]
    }
   ],
   "source": [
    "# time step\n",
    "dt = 1. # [h]\n",
    "# AR(1) input : std and correlation\n",
    "p_scale = 1. # MW\n",
    "p_corr = 0.8\n",
    "# Storage ratings\n",
    "E_rated = 10. # MWh\n",
    "P_rated = 4. # MW\n",
    "print('Energy storage capacity: {:.1f} MWh'.format(E_rated))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables of the problem:\n",
    "\n",
    "* 2 state variables: $E_{sto}$ (stored energy) and $P_{mis}$ (production mismatch)\n",
    "* 2 control variables: $P_{sto}$ (power absorbed by the storage) and $P_{cur}$ (curtailed power)\n",
    "* 1 perturbation: $w$ (innovation of the AR(1) process)\n",
    "\n",
    "$$\n",
    "\\begin{split}\n",
    "  E_{sto}(k+1) &=  E_{sto}(k) + P_{sto}(k) \\Delta_t \\\\\n",
    "  P_{mis}(k+1) &= \\phi P_{mis}(k) + w(k)\n",
    "\\end{split}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.0, 1.62)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def dyn_sto(E, P_mis, P_sto, P_cur, innov):\n",
    "    '''state transition function of a lossless Energy storage\n",
    "    \n",
    "    returns (E(k+1), P_mis(k+1))\n",
    "    '''\n",
    "    # 1) Stored energy evolution:\n",
    "    E_next = E + P_sto*dt\n",
    "    # 2) Storage request AR(1) model:\n",
    "    P_mis_next = p_corr*P_mis + innov\n",
    "    return (E_next, P_mis_next)\n",
    "\n",
    "# Test:\n",
    "dyn_sto(5, 2, 1, 0, 0.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Probability law of the innovation $w(k)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "innov_scale = p_scale * np.sqrt(1- p_corr)\n",
    "innov_law = stats.norm(loc=0, scale=innov_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set of admissible controls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    \n",
    "Contrainsts of the Energy Storage are:\n",
    "\n",
    "1. Energy stock boundaries : $0 ≤ E(k + 1) ≤ E_{rated}$, therefore $-E(k) ≤ P_{sto}\\Delta_t ≤ E_{rated}-E(k)$\n",
    "2. Power limitation : $-P_{rated} ≤ P_{sto} ≤ P_{rated}$\n",
    "\n",
    "Also, the curtailment $P_{cur}$ is constrained between $0$ and $P_{mis}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((-2.0, 4.0), (0, 0))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "curt_activ = False\n",
    "\n",
    "def admissible_controls(E, P_mis):\n",
    "    '''set of admissible control U(x_k) of an Energy storage\n",
    "    The two controls are P_sto and P_cur.\n",
    "\n",
    "    ''' \n",
    "    # 1) Constraints on P_sto:\n",
    "    P_neg = np.max((           -E /dt, -P_rated))\n",
    "    P_pos = np.min(( (E_rated - E)/dt, +P_rated))\n",
    "    U1 = (P_neg, P_pos)\n",
    "\n",
    "    # 2) Constraints on the curtailment P_cur\n",
    "    if curt_activ:\n",
    "        U2 = (0, np.max((P_mis,0)) )\n",
    "    else: # disable curtailment\n",
    "        U2 = (0,0)\n",
    "    return (U1, U2)\n",
    "\n",
    "# Test:\n",
    "admissible_controls(2, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantaneous cost function, which penalized the deviation from the grid commitment $P_{dev}$.\n",
    "\n",
    "Here we choose a *quadratic* penalization, with a tolerance of $\\pm 0.9 P_{tol}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(0.3025)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P_tol = 0.5 #MW\n",
    "P_tol_reduced = 0.9 * P_tol\n",
    "\n",
    "def cost_thres_quad(E, P_mis, P_sto, P_cur, innov):\n",
    "    '''a threshold-quadratic cost model:\n",
    "    c_k = (P_dev - P_tol)² if P_dev > +P_tol\n",
    "          (P_dev + P_tol)² if P_dev < -P_tol\n",
    "          0 otherwise\n",
    "    '''\n",
    "    P_dev = P_mis - P_cur - P_sto\n",
    "    # Cost when P_dev is above/in/under the threshold\n",
    "    c_abov  = (P_dev - P_tol_reduced)**2\n",
    "    c_mid   =  0.*P_dev\n",
    "    c_under = (P_dev + P_tol_reduced)**2\n",
    "    # Choose the proper cost:\n",
    "    cost = np.where(P_dev >  P_tol_reduced, c_abov, c_mid)\n",
    "    cost = np.where(P_dev < -P_tol_reduced, c_under, cost)\n",
    "    return cost\n",
    "\n",
    "cost_thres_quad(5, 2, 1, 0, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collect all these function in one `SysDescription` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dynamical system \"Storage + AR(1)\" description\n",
      "* behavioral properties: stationnary, stochastic\n",
      "* functions:\n",
      "  - dynamics    : __main__.dyn_sto\n",
      "  - cost        : __main__.cost_thres_quad\n",
      "  - control box : __main__.admissible_controls\n",
      "* variables\n",
      "  - state        : E, P_mis (dim 2)\n",
      "  - control      : P_sto, P_cur (dim 2)\n",
      "  - perturbation : innov (dim 1)\n"
     ]
    }
   ],
   "source": [
    "from stodynprog import SysDescription\n",
    "\n",
    "sys_desc = SysDescription((2,2,1), name='Storage + AR(1)')\n",
    "sys_desc.dyn = dyn_sto\n",
    "sys_desc.control_box = admissible_controls\n",
    "sys_desc.cost = cost_thres_quad\n",
    "sys_desc.perturb_laws = [innov_law]\n",
    "\n",
    "sys_desc.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Control optimization with SDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a `DPSolver` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDP solver for system \"Storage + AR(1)\"\n",
      "* state space discretized on a 41x61 points grid\n",
      "  - ΔE = 0.25\n",
      "  - ΔP_mis = 0.133333\n",
      "* perturbation discretized on a 9 points grid\n",
      "  - Δinnov = 0.447214\n",
      "* control discretization steps:\n",
      "  - ΔP_sto = 0.001\n",
      "    yields [4,001 to 8,001] possible values (6,342.5 on average)\n",
      "  - ΔP_cur = 0.1\n",
      "    yields 1 possible values\n",
      "  control combinations: [4,001 to 8,001] possible values (6,342.5 on average)\n"
     ]
    }
   ],
   "source": [
    "from stodynprog import DPSolver\n",
    "dpsolv = DPSolver(sys_desc)\n",
    "\n",
    "# discretize the state space\n",
    "n_E_sto = 41\n",
    "n_P_mis = 61\n",
    "p_mis_max = 4*p_scale\n",
    "dpsolv.discretize_state(0, E_rated, n_E_sto,\n",
    "\n",
    "                        -p_mis_max, p_mis_max, n_P_mis)\n",
    "# discretize the perturbation\n",
    "perturb_max = 4*innov_scale\n",
    "n_perturb = 9\n",
    "dpsolv.discretize_perturb(-perturb_max, perturb_max, n_perturb)\n",
    "\n",
    "# control discretization step:\n",
    "p_sto_step = 0.001 # MW\n",
    "p_cur_step = 0.1 # MW\n",
    "dpsolv.control_steps=(p_sto_step , p_cur_step)\n",
    "\n",
    "dpsolv.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Policy optimization with \"Policy Iteration\" algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1:** Create an empirical control law, to initialize *Policy Iteration*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 1, 0])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def P_sto_empirical(E, P_mis):\n",
    "    '''empirical storage policy\n",
    "\n",
    "    P_sto = P_mis \"whenever feasible\"\n",
    "    '''\n",
    "    # Compute the contraints on P_sto\n",
    "    P_neg = np.max((           -E /dt, -P_rated))\n",
    "    P_pos = np.min(( (E_rated - E)/dt, +P_rated))\n",
    "\n",
    "    # \"whenever feasible\" control:\n",
    "    if P_mis < P_neg:\n",
    "        return P_neg\n",
    "    elif P_mis > P_pos:\n",
    "        return P_pos\n",
    "    else:\n",
    "        return P_mis\n",
    "P_sto_emp_vect = np.vectorize(P_sto_empirical)\n",
    "P_sto_emp_vect([0, 5, 9, 10], 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate this policy on the state grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 61, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_grid = dpsolv.state_grid_full\n",
    "E_sto_grid, P_mis_grid  = state_grid\n",
    "\n",
    "grid_shape = dpsolv._state_grid_shape\n",
    "pol_ini = np.zeros(grid_shape + (2,))\n",
    "# P_sto law: (and P_cur = 0 implicitely)\n",
    "pol_ini[...,0] = P_sto_emp_vect(*state_grid)\n",
    "pol_ini.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2:** Assess the number of value iterations required for the *policy evalution to converge*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
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      "policy evaluation run in 0.06 s     \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "J, J_ref = dpsolv.eval_policy(pol_ini, 50, rel_dp=True, J_ref_full=True)\n",
    "plt.plot(J_ref, '-+')\n",
    "plt.title('Reference cost evolution');\n",
    "plt.xlabel('number of value iterations');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion : 50 time steps (i.e. 50 hours) is well enough for the cost to converge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3:** Run *Policy Iteration* algorithm\n",
    "\n",
    "note: each iteration takes about 5 s (on a good Intel Core i7 laptop), mostly with the value iteration step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "policy evaluation run in 0.06 s     \n",
      "ref policy cost: 0.105724\n",
      "policy iteration 1/4\n",
      "value iteration run in 5.50 s\n",
      "policy evaluation run in 0.06 s     \n",
      "ref policy cost: 0.0486519\n",
      "policy iteration 2/4\n",
      "value iteration run in 5.21 s\n",
      "policy evaluation run in 0.07 s     \n",
      "ref policy cost: 0.0468464\n",
      "policy iteration 3/4\n",
      "value iteration run in 4.90 s\n",
      "policy evaluation run in 0.06 s     \n",
      "ref policy cost: 0.0468268\n",
      "policy iteration 4/4\n",
      "value iteration run in 4.89 s\n",
      "policy evaluation run in 0.05 s     \n",
      "ref policy cost: 0.0468268\n"
     ]
    }
   ],
   "source": [
    "# Number of policy iterations:\n",
    "n_pol = 4\n",
    "\n",
    "# Number of value iterations\n",
    "#(inner loop used for policy evaluation)\n",
    "n_val = 50\n",
    "\n",
    "# run\n",
    "J, pol = dpsolv.policy_iteration(pol_ini, \n",
    "                                 n_val, n_pol,\n",
    "                                 rel_dp=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "\n",
    "* convergence in only 3 step policy improvements\n",
    "* significant cost improvement: 0.106 → 0.047"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the energy manangement policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the policy from the viewpoing of $P_{dev}$, the deviation from the grid commitment.\n",
    "\n",
    "From the control optimization, we can extract the optimal law $P_{dev}^*(E_{rated}, P_{mis})$ as a 2D array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41, 61)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol_sto = pol[..., 0]\n",
    "pol_dev = P_mis_grid - pol_sto\n",
    "pol_dev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Colormap for the plot: Cynthia Brewer's [RdBu colormap](http://colorbrewer2.org/?type=diverging&scheme=RdBu&n=11) (reversed so that negative values are blue, positive values are red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01960784, 0.18823529, 0.38039216, 1.        ],\n",
       "       [0.96908881, 0.96647443, 0.96493656, 1.        ],\n",
       "       [0.40392157, 0.        , 0.12156863, 1.        ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm_blred = mpl.cm.RdBu_r\n",
    "# Test:\n",
    "cm_blred([0, 0.5, 1]) # → blue, white, red"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D plot of the policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "surf_opts = dict(rstride=3, cstride=3,\n",
    "                 vmin=-p_mis_max, vmax=p_mis_max,\n",
    "                 cmap=cm_blred, edgecolor=(0,0,0,0.2))\n",
    "azim = -20\n",
    "P_mis_label = 'Mismatch $P_{mis}$ (MW)'\n",
    "E_sto_label = 'Energy $E_{sto}$ (MWh)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,3.5))\n",
    "ax1 = fig.add_subplot(121, projection='3d',\n",
    "                          title='Stored power $P_{sto}$ (MW)',\n",
    "                          xlabel=E_sto_label, ylabel=P_mis_label,\n",
    "                          )\n",
    "ax1.plot_surface(E_sto_grid, P_mis_grid, pol_sto, **surf_opts)\n",
    "ax1.locator_params(nbins=5)\n",
    "ax1.set_zlim(-p_mis_max, p_mis_max)\n",
    "ax1.view_init(30, azim)\n",
    "\n",
    "ax2 = fig.add_subplot(122, projection='3d',\n",
    "                          title='Deviation power $P_{dev}$ (MW)',\n",
    "                          xlabel=E_sto_label, ylabel=P_mis_label\n",
    "                      )\n",
    "ax2.plot_surface(E_sto_grid, P_mis_grid, pol_dev, **surf_opts)\n",
    "ax2.locator_params(nbins=5)\n",
    "ax2.set_zlim(-p_mis_max, p_mis_max)\n",
    "ax2.view_init(30, azim)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2D plot of the policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_opts = dict(interpolation='nearest', cmap = cm_blred,\n",
    "                   extent=(-p_mis_max, p_mis_max, 0, E_rated),\n",
    "                   vmin=-P_rated, vmax=P_rated,\n",
    "                   origin='lower', aspect='auto')\n",
    "P_mis_label = 'Production mismatch $P_{mis}$ (MW)'\n",
    "E_sto_label = 'Storage energy $E_{sto}$ (MWh)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b16zGeehRK7JtUVseYs1xU3jw9V3s78q/YTSEcoJ2sU5qis1xYkzkMlLKBkU7oEJ8lrr59XyrkVd6zzmPsrvutCY/FHJDIPv7LQweU9ph1x7vfgVGWdDPeQun8ereDjbsy/+2UzWDzNEeng/Vl/yg6u6hTi75rVBeXUvvkcPjFhZYiMQaGnA6DlsLQwVYeeoKfvyTn9oR7jhWdbeF4winz2mmNxJl/c78F5loIELbYA8vB2vzrYp1CnnJX5RWfoBY3KBR2zaNfTu30jBl5tFjjjHAOl7jbYqxWEY7jCNm7sfMU/ANYT4MMnXPUYXes84h/Oc/0XveBRmn8/OiV33c9MG/4iN//Ve849obqMyteHTpScgzz6MrTsn8ZA+rvhrHU26NedyMUTas8jjGccfhxDkVPLFhN0fUT7XZ38x/GknenhJDnhNMtrQ7keQHjd8IVTWZ5utFcdgebmBGMDkPgxNI/qn7w0aoaYWRzs8IVTW9AMzQ1aDRP2y8LksIXQ30ZGHlZ9LKb5Xalqkc3rMj32rklb5Vqwk/8bg1+VOmTKW7q8vKSkBXr0Tuf8i7YwEzs6ma3YfHL93faEynjwMEGCxRy7+Q3nJ/csk/RkSEqvpmjuzfm29V8ofjoKEg0ttr7RJtbe08++wzuRccDkF5ORzK/7J5rDRWlbG/0969z5Tj6GGDL9dricJAZNIoZZ2mGXPZt21jvtXIKz3nnk/ZvX+0Jv/G976Xb33zG1Zkx95+Jc5PfmVF9njgc5zUjGd5pIZBusVPpARnqZNuU+OAOA7hiip6Oy3l7ywCBpYsJfTcs9bkrznzLDZttPTQam2GAwchMrFd4HLJ/Ggnr/tyUiap4CjkJX9RGqVUk3OgOgitcxaw/eVnmXniitSCYRnmR/XKq5kNKQYur/7p5mYVIVZRgXQeQauqx6hdKkP5UQH8wRD7eqI01Ncf06cvy4dY3DCkl16I3HUvevmxUNSU/KVes0Cv0FOP/ipGDarA6EagxNDT8lCQ7qhQGQ6M2N3Mt2qGpmp/cnhowJ983BcYPdtVYmG+mmgvEfHT5wQpJ4bPMEpF+5KNUmJM6UzDrC+Y/KMJlI0+dIwmz+mYNEoVPD5/oKBLa4wH3Ve9g8qf/Mia/PMuuoS1t91uRbaetBh58WUrsseDxdOaWLe1sLJnLdZOnpdqekrnZw4U9gy1tO70BGdwxkz827dbS+n3rvdcz5/ve9CKbACammBvcdZpLAv6aagIsXMci/t5EUBZrh2sl2oOOR6z7SJBRPD5nbRaPiipAVUcHzGL+UGLgd5zzqPs/j9bkV1RWclgdJCopXscu/winN/eZUX2eLBoSj0v7T501Ee6EPCjrNDD7PBXsNVfApZ/AccnaTVPUSJhEXlSRJ4XkZdE5F+yVa+kBtRQeQX9PV35ViOv9J22mtCT9nxSFy88nt/d+Qc7wltbYG9hLZszQURYMq2Bp7cVVgFCB1g8cAhQXgjWFXUiFQHE56TV0qAfOFtVTwKWABeKyMps9CtKoxRokuFpaEYQLK+it6uTyuqapN6mS4sZOeV47Lek1AA0jo8WOWX+Wb22drKOnHJ8EFV0MAo+n+f1vPKjmp/9Qx//O/7m4/+HS666GoBgtkYpAz1uHrzyOhw/37tzhvlRPY2NKf3NfKpmpJTx2h+guS7A1kM97OkaoLVy9GW2mW81VZ3RI6kCRr7UxEHEF0z+aUeCAeYDBxWeKWtnUdkhKji20hg086WGzXyqyZFU5gxQzDy4BtEE42ZWybol1eA1VtT9sQ3NwALxltXyoqRmqOU1dXQfKqzZQT7oP/Ekgi++YEX27Nmz6ezsJGYpJ6ievgpnnYUAgnFk2awW1m/fT38B1j6rl0FOlU5edYrUpUrSW+7HB/xGEVmX0G5OFSc+EXkOeBO4R1WzSq1WUgNqMFxOpL9wIlbyRd+pqwhbXPbPmDGTxx61lHavphqO5D+DUzaICKvnt/PIhj0FmbjHLyBFmpdKBHwBX1oN2K+qyxLaWlOeqkZVdQkwFVghIouy0a+kBlQAx+dnMJK/YmqFQKy5GWe/vb3IG264gW/f/m07wsMh6Mt/ntFsqQgFmNVYzYu7CjOk1o8WbSSV+CStlgmqehi4H7gwG91KbkBtmjmPPRtfzbcaeUdUrfnlnn7GGWzZbKlIYgEUw8sVsxqr6OwdYH+XRx2uPFCvAxyUInSlEsmZUUpEmkSkNv7vMuBcIKvBoyiNUgoJBfqS0/UFK6rp7ekmMhjFiYdEmdFF5vZfTEa3OuW6iF82pBs5FZkxC//WLURnzcrp9bsi7s1x/AF2HzjM9DLDMDJoDB7pFu07KsBxB1VxhknHl2Hk1HCykwR6FPkzTzdD7PwjR0WB+z1dOX8q97+6g7MXTgNj5STm+WUVya89jGA+83jitaPJn81M39fQ18tmp4IWdVcDplHJHJDMSCkzfZ+JOUN0EuQ5O7KLlErHJSpN2oDviIgPd3L5E1X9XTYCS26GCtA8cx5vbrFXFqQY6Fu5kvDj9sqLnL5mDT/+4fftCC/ChNMj4ThC0O8QKTADVRkxOooxzZ+4YazpNC9Udb2qnqyqJ6rqIlX9TLbqleSAWlnXSPfhAwVpEBgvBmfPJbDJXgaua298L/fcbckftabGTZZSIjRXlXOgAJf9J8U6eMqpLa69VBF8QV9aLR+U5IAKUN8+nYO7iqeiZs6xvBfZ3NxCd3e3lYdW7OLzcO60l4pwvGmsKmNfAeVLHaKcKEtiHaxz6jgio29fFAoidoxSuaJkB9Ta1slM/oOtbfh277Ymv7m5hZdesWAAnD4NduzMvdw8UVsR4lB34c1QwV36r4gdZGugko2BqqJwpnJ8TlotL7rl5arjgIhQWd9E54HiTLaRC/pWrSb82CPW5F919dWsvd3SPmpjA7xZvGGoiXhF4uUbH25oakVskKdDjfQX8rAg6c1O8zVDLUorP5CUgCKWYsV3jzVMm83W55+ktrF5VFleRfx8HuGYpt068W+ZYpU3+pq65/KrHDluAZW//kVG53iFovYPHjv+lnMv4tavfZVY2bFQX19nbmZisYvPx7nrHmI3vDv9kzys9BkX8TNPl9FDUcX4NengMat+TEH9gdEH11Gs9q6QZMNWonxItuT7PAr6mVb8aHiAGUCrDvBcuJUZ5Udo1GPyzdBUcZJzs4pvdN/vWCRB9yweMIJr6CtUCvhRlD2O4yNYXkFv15F8q5IfnFTXo1wSCATw+fwcsGFAmtIO20tn2b9wSj3rthT+jDskygrpZLuU5VuV4REmjVL5pHnmfN7csiHfauSNaEMjzj57P+R3vP0qvvjlr1iRrYsXwvqXrMgeb5qry3FE2NMxetb9QqCgdygKfMlfEAOqiPyfeD7CF0XkDhEJe5+VHoFQmMhA/4R1oepbcyZl9/3Jmvzrr7uWxx57zIpsvfA8nD/cY0V2PjhlRiPrdxwkEi38BHqF+mtxrfw5S9+Xc/I+oIrIFOAjwDJVXYS7R/6uXF6jtqWNw3t35VJk0RA5fiHBV+yVFgmFQiDQa6OEdSgIPj90F04W/GwQEVbObuaxjYVf8lxItQ0UCrlKMG2DQjFK+YEyEYkA5cCoo58qRBKsPeYmtWPMRmtbp7P5+SeobpniHvcIRTUjDKOGZcnvsSmuCc93ydJp2is/qlcoqioMNjfj7N5DtLU158u5XvVx2ulr+PYPfsRN73s/njnhMwlFFYfY5Rcjd96LvvOt2YeiZlrEz+N8MxRVDaOR+JMNQwpUVwVoqevn9f1dzG807pZxL1I+jWkEC468kPP6YZuhqbGB5Oi0Sh2kCz/VuO+boamm0SvgEVKcOGPMarCT1AKChUTGM1QRqYjHvuYEVd0J/BewDdgNdKhqile3iNw8lNfwyKEDGV1DHAcRmbDL/u4rr6IiQ2t/JnzgLz7Ib3/9azvC581BNm2xIztPHNdWz46DXQW99G/VfrY7hWeYkmKPlBIRR0TeLSK/F5E3cbOx7I7vef6niMzLRgERqQOuAGYB7UCFiFxr9lPVtUN5DavrGjK+Tnl1Hb2duc0uXyxEW9vw7bW3zGxqbqanp8fOA0vEXX+W2MNw6Yxmni2wUimJVDFIBIe+/O8KJlMCe6j3AXOATwKtqjpNVZuBM4DHgc8NNwBmwLnAZlXdp6oR4BfAaVnIG5aqhmY69xf+3pUtIgsWELC4lzp12lTWPfmkFdk6fy68YS8vQT6orQjRG4nSFyncRDDHxzp5ueAy+4u74kyj5YN0rnquqv5rPDPL0TWKqh5U1Z+r6lXAj7PQYRuwUkTKxd0gPAd4JQt5w1JWXUtP5+Fciy0aui++jIo7f2tN/o3vfR+33nqLFdl62qk4j9irQJAvTpnRyNNbC3eWGiJGGVEOUThx/iKFHXrqaZSKzxoRkRBwFTAz8TxV/cxQn7Ggqk+IyM+AZ4BB4FkgpVTBaJhle0eKnNKYEotpynGfYdxIKepnHE9ZfHpWf0uflAKAYxeVhFZWIr19EI2mWt2yoCtefO2kFav5l09/mlg4uUCik4sifi3NY6uGmusifh5GsdT8paMbqSrLYiAOXZEYlaFAisHONDqZRft0IPl1knaGkcgxZsLmktgxivr5YyEATtAIj2kNy4lk9F0UJzlyKtEIlp1RSfK2nE+HTDT7Ne5e5yDQndCyRlX/WVUXxHMSXqeqVmpgOD4f0RLKtZkpfatPJ/zIQ1ZkiwjhUJiDhyyV/PA53smpi5ClM5pYv71wZ6mOwELpYb1T4915PCiBPdQhpqrq1ar6eVX976FmTTML1DRPoWNv6YQzZkrvmjMJP/iANfnnXHAha2+93YpsnT8PXiu9iLeyoJ/BArb2A9RJlEbt53XxdIqzjwhOIJBWyweZDKiPishia5qMA9WNLRw5MHENU/h8aCiE9NgJf3zXtTfwp/vsDNi6fGnRl5cuZqZqH6DsyF0Q45gQcldTygbpuE29ICLrgdOBZ0TkNRFZn/B+0SCOu2ycqP6oAL3nXUDZn+yEc1ZWVTEYHSQatVDuo70Vdu/JvdxJ0ma+dnNAghzIp5FKwHGctJqnKJFpInKfiLwSdwP9aLbqpRMpdWm2F8k1bpG+hEgpM1pIR46cKq9v4tC+PdQ0tQ17HEixankV8TMjpxJtZI5xbszULVmUZySTV+SUF/2LT6Lszt+hl16R1vW80vmZt+74RYv5zb0PcNlllwMQ9DJKZVrEzya5LuKHESllpNs7+tlFUBEkMHrKvZRpgCEvyWhlFAD0hUNGX8MoZRTxixqvh/qfohEei9WyJDqAPzEi0MP9K8nRPstApxzOPgeBv1HVZ0SkCnhaRO5R1TH7F6aj2VuBZmCnqm4121gvnC/qp8xk39bS8mnMCMfJvFpoBnzkIx/ltttusyO8rRV22atAkC/Kgn66+4vDWCoCi5weXvPlyT81h2WkVXW3qj4T/3cnrrvmlGzUS2eGOhX4MrAgvsR/FHgEeExVi66SmjgO1Q3NHNm/h+rG1nyrkxci02bg37qFwRkzcy577ty5HDlyBFXNePbsRez0VcgDj6DXvD2ncvPN8e31PLNlL2vmtuRblbSolhh9EiTG+GdXGhSHZ3216XZvFJF1Ca/XquqwLpkiMhM4GXgiG/3S8UP92/gFg8Ay3Cim9wK3iMhhVV2YjQL5oGnGXDY9++iEHVB7zzmX8rvvovOm91uRP2XKFNatW8fy5ctzK3jWDOTHvyjY1HJjpTIcpKY8xI5D3Uytq8i3OmnRGutjrxOmLTa+tbICoiwLpG1U3a+qy7w6iUgl8HPgY6qaVTb6TB4wZUA1UBNvu8hyNM8X4jhU1k3celPR9in4d9lLZ3j9dddx263fsiPc74f+0cttFCMnTmvk5V2HiBaJr21brJfdeUqekstIKREJ4A6mP1DVrDMIec5QRWQtcALQiTuAPgp8QVUteXCnR3JNKRnxmHs89dzG6XPZ/PzjVNQ1pRw3CXjsoucwUCq1XlWGNae80vkdvU4oiPT1QVlu3WC6IjGWn3EWn/33z9EViVHnSza0SDSLwVAcdNUK5Mln0DWnZZ7Ob5xrTqUYqWJmJNWx1wIsm9XCU1sPsHKOu3JKMVKZ6f3MdH4J/VNSCRq6phwWrk0AACAASURBVBhDPYxK5p5kMBzEh4P4g6QTd5d4fjZx9iKSszj9eKj7rcArqvqFXMhMR7PpQAjYA+wEdgBFHxQvjkN5dR1dhwo3SsUmNiui+nw+/H4/3V1dOZetpy5DHi3KhZEn9ZVhHIH9nRaSdVtgKv3sYPz9UnPoh7oauA44W0Sei7eLs9HN86qqeiGwHDdnKcDfAE+JyB9F5F+yuXi+aZ41n31bXs+3Gnmhb/mphJ6yNzCddvoZ/PiOH+RecCAA4RBYGKwLgVNmNvPstsIv5gfQzAA7CRHJ1g8qE0RwAv60mheq+rCqiqqeqKpL4u3ObNRLaxhXlxeBO4G7cK38c4CsHWHzieP4CFVU09s5AauiBoOIxbwG1954E3ffmdV3c0Ril16A/P5uK7Lzjc9xmNlYzaZ9hZ+7V4AldPI0VeNmKBQRHJ8vrZYP0omU+oiI/EhEtgMP4jr6vwa8Dai3rJ91GqfNZv+OzflWIy/EKiqRzk4rstva2unpsTSLnDsH2Vi6f7O5zTVsfLODWBFE9JUTYx69vOAbv+QpRR16ipuu72fAClWdHc8G9TVVfT4xP2qxEiwrZ6Cv8Ev72qDf8rK/obGJl1+1tKUypR227bAjO8+ICCdMaeClXXm1+6ZNAxHqdYA3fOOQPKXYs02p6sdV9WeqWjAhKqpKJJbQorHkFks+HlWMpknNHy6nt6fn6HETs3/MaKn6HWsxo5l4HbdJ/9JTCD29zrtjAjE0qaXIHNSj7fK3vZNvfv8nxMpqjrZcEbv8Ypzf3pUzeTlBnOTmhT+Q1CShTWmsZV9XPxEV1xvA8bmhqglNjHMSZTmhsqRmXsvEdDnyhYNJzW80XziU1GYGo0jAz55wFf5wkEBFOKkl7m1mF/BR2Bn703Gb+s1ox1X18typkx8aps7iwI7NtM07Id+qjCsaDiMRez6d5110Cd//1tfsCK+rhY4jOU+YXUicPL2RF3YcYOmMpnyrkhYL6OEpqmkkYq2csjiSkgy7kEhHs1XAduAOXD/Uwq3hOkbKKqvZ021nL7HQ0XAZ0tODlpfnXHYwGMTv93PgwEEaGnK/3a5nr0H+/CB63lk5l10I1FWEeWFHZhV+880setlNkGnYi6DK1+wzHdLRrBX4R2ARbkz/ebghXQ+oqr1sxeONCFokUSq5pH/ZCqv7qG+/6m18+av/Y0W2nroMeTyzLYtJ7FJPhIM20/uJII4vrZYP0tlDjarqH1T1BmAlsAG4X0Q+bF27caS6oYUjEzAUte+01dbKogBcf+17eOTRR+0IF0GPnwcvv2ZH/iQZMy5zx6N7yh4tD6S1GREv0HcJcA2u1f8ruOWe84JCkjHINCSZz8d0QlGrm9rZ+fp6qhpahjluxn8aOUIN+b7EeE9DNzXyo0qGOygpRfw88qN6haKqz4/6/NDb5+6p5npDJ1SJisPB7gEqKirw3FhIXM55rRjEQS+7BOfr3yJ2wvGZh6IOI280UkKMPZxcvIr4mbdaHVOeUfvSPN8/cv5UM/TUxCwAaA4/XstqczUXSwhdlZikZKLyJe57ZmmUSsmhW0Ck44f6Hdz4/aXAv6jq8nhZ6ZIqzuQLBIhFxly8tajpOf9Cyu62ZzE/66yz+c7t37YjPByCgdJLljKE3+cQsVEBwSK1ROkQS8v+eCLudFo+SGeovw6YjxsV9aiIHIm3ThEpqRAjfzBEpH9805EVAgMnnUzo+eesyX//B27mD5aipgCoqIDunBTgLTim1VeydX9xGUynSz9v+CxFTwkFveRPZw/VUdWqeKtOaFWqWj0eSo4XtW3TOLynNJ3FR0WEaEMDzn47iWLqGxro6+uzVstLl56EPP28Fdn5ZmpdJVsOFNeAGhBlWqyHzU7uc7tKgfuhprPk99zwSKdPMVBR20D34YmZfarn8iup+M0vrcmfPmM6jz9mxzilJ5+IPFuaA6qIuIPq/uJaDLbF+uhwAvSkldwvAwp8hpqOUeo+Efk58GtV3Tb0ZjyD/+nADcB9wO1WNBwGVYgkWKIcYzyPGjOhTIr4xRQGY8nlOzIt4ucbxdCUaiQaPZtqSkpPS4+uwWnT8e/YnnO5XRHXeHH19e/llm/dylnf/HLScac/B7OvsjLoG8NWjVdRvkzP9+puBCCkzNcdI4dpPKJpwdQm7nlhC9MbqpK+6ynnJxqijAHFCSUngzY/aab5Ur2K/AUqyjhZB1gXaGCldBr5ULM1ShVuIEc634gLgShwh4jsEpGXRWQT8Aau1f+Lqnq7RR3HlaqGZjr2FUyU7bgSmTUH/xtvWJG98rTVbN26xYpswM3kX6JGRRFh0bQmXiwyJ/+AKDOknw2aw5ypIikhtyO1fJDOHmpfPBnKamAGcA6wVFVnqOoHVNWeNSMP1E2Zyf5tm/KtRl7ovuJKKn77KyuyRYRQKMTBQ3YSfujihcj6l6zILgTa6yo50NXHwGBxWfzbZYBOfBzJWTCqFPSSP6M1i6pG4qVXiz5j/0iICDVNrRzea6/mUqGi1TU4nZ3e/p9j5JzzL+CWb3/XimxduQJ5/CkrsguFZTObeWpz8QWfnCTdvORUpWwzjAlxt07SafmgcD1k80jj9Dns377JmlW6kOk7dRWhp560Ivvd193IvX++34psqqugqzRdp4aoKgvic4TDPf35ViUjfAILY5284OTCKUhSsm6N2LwkidwmIm+KyIs5UAxIM1Kq0FAgkjCLChhGpkg0JZ7IeJ18PGAsD1Shprmdg7t3UNc6NSVyyudhGYomWJ58xga813Z8ukX2RsJ8CGQaOdV7xluo/sb/0rdiZfz8zK5vkvinKK+sdlMqBirwxWcQoxqlzB+F18y5LAw9PWAh0QuQYoTyLM7oFbllGqnMAgrmstWJcsrsVh54ZQfnLpqeOgtL7G/sIaYU7TOirLzyWPgMo5OGjeKLRv7Rwb7kYIv6aC8HUPb6yrNL3zdk5c8NtwP/A+Rs2TSmGaqI/KuI/FREbheR43KlTCFRP2UmB3dumXCzVC0vx+mzVyTuhBNO4Pe//70V2briFOTJZ6zILhQCPocZjVVs2Ft8u25z6WV71kX9cmeUUtUHgYNZKpTEWJf8tar6DuBm4CM51KdgEBHq2qZxaPc2784lRqyyCrFUZ+vDH/4wt99+uxXZuvQk5JnS9EdNZF5rHVuKoOaUiQAnk6WbXIFnmxrrkn9ARE4GXgRyHw5RINS1TWfTM4/QPHVGllnGi4u+VacRfuxRes+/MOey58+fz6FDh1DV3N/TYBAGS9N1yqSuIkxn3wBV4fzErI+VYJYBqQODMR58PW23xkYRSczvuFZV12algAdjHVA/BXwYd3b649ypU1gMzVIP791JXevUfKszbvQvWUrtF//TyoAK0NLSwosvvsjixYtzL7y8wjVOVZbscx6AKXWV7Dzcw4LW4hpQsyUY8LFm4fR0u+9X1WU29TEZ65L/r1T1P1X1JiBrp00RqRWRn4nIqyLyioisylZmrqhtmcqhPSWVWMubQMBuielrr2XtWjsTBV22BHm6pFyjh6Wpuox9nfb2ugsVQQrabSqjGaqI1AJfBBaISB/wPPB+4KYs9fgy8AdVfXs8pDUjM23UMF2boahmPlTTtB1JOZ74b4doLJYUjmqGopqhrYmYYagpUaxm/wxXwV7+DGMlWl+Pc+AA2tjgcX3jXnj4MXRFYpx+zvn81xe+SFckRo3PsBZHs0jFJw669GScr34DPfOM3OdHzXVoqhqWd9PbJEXcseN+x5daIDLBKyIlV6ppxTdeq+lRYRh1zE/i8/AKcMxQ1cQBLisrv8AoeWAzEyV3AGfibg3sAP5ZVW/NRmZGA2rcof8mEbkA2A+cSJaJpkWkGlgD3Bi/xgBQUAkuq+qb6Dq4n6qG4iiWlgv6Vq8h/PCD9F751pzL9vv9+HwO3d3d1OTacS8UdAv3DQxAoCi9AtNGEGIxxckqNr74yFUmKVW9JieCEhirZhFVfRr4A5BtosvZwD7g2yLyrIh8S0RSNsBE5GYRWSci67oOj29Mc13b9Aln7R84YRHBV+yFcq487XR+9pMfWZGt55+D3P0nK7ILiSl1FWw/1JVvNcYXKaHQ0wQuFJGpwDdwtwCywY9bDeDrqnoy0A38g9lJVdeq6jJVXVZZO/oyNNf4g0EGLZZbLkgcB7FYtPDG972PO387aoXyMaNLFiPPrbciu5CY2VDJ1gMTbEAFdyslnZYHxuyHCvw98Akg2zi4HcAOVR0qvfkz3AG2oAiVV9DfM7G+vLHyCqTLzmdua2uny5JsRND5c+E1O5mzCgWf4xBTnWDBJ1LQA6rnJpOInKCq5trvM8ACVX1NRLJKf6Oqe0Rku4gcp6qv4WazetnrvMSQxkhKTtHRTTUB0zCURhG/urYZ7N++hbZ5J2RUxM8s4Of32O9SwxQRM3O3Gv1tusf2n7KM0DNP07fmLWmf42Wk6o0cO17X0MSLm3excOHxR9/zde0bo7bJ6OUX43z1m8T+bl76J3kZoTyOq3HcehE/f4CWuir2dEdoq61I6q/misqMHBpMPj5awT9INZA5geSffXQgg3ypWaZDVadw98bTGca/N/QPEXk/gKruUNV7RaRcVVOW52Pgw8APRGQ9sAT4txzIzCnhymr6u4urFEW29J98CqFn7NW9f+vb38ktt95mR3hZGfh8JZ8wZU5zTVGGoY4dcWcR6bQ8kM6AmqjZXxnHclLQXVWfi++PnqiqV6qqnaSZWeL4/EQt+mcWGlpZiVgsfnfeRZfy/Hp7e52xKy/B+dXvrMkvBIJ+d1bbMzAxIsSAnGWbsqJaGn0SZ/telRJKmpqWdjrenFh5UjUcRnp6rMgOBAIEA0EOHMhpfopjzJ4JW7alOv+WGEtnNvN0EeZJHQuKu7WSTssH6Vy1VURujMfup2zrWNCpYKlubOXI/j35VmNc6Vt9BuEHH7Am/21vu5Ivf/V/rMnXJYuR53OW7rIgqQgF8DsOR3ongCeKFLlRCvg0sAw3GmqqiLwEvBpvjfZUGxlVTcqHaj4XPPOVekU6GWa2o4X0RIjFYkTNAJXRiviZdgxTGc+kmpmRYo7LMj9q34qV1H/6n+i54KL4+dnpZ/Ku627iissupR/XaDJqiFym+VHFQS84F+d/16Inn5R95NQw8pMFehixTIz+GRfxSzBinTK7jUdf28aZC+I5J1L8MJO3BLyMUCmY99pIQuMLpm8oysYxX7p6cL7wtTGfbxvPu2BmZ4n7n54ILAYetKRXwVJWXUv34QNUjLMvbN4QITJnLoENbxCZm4HFPE1CoRCOI3R2dlJVVZVz+QSDECn9fe9QwEdlOMj+rl4aK8u8TyhStLKC6N99LL3OX7C38hmJjB8VcQv/nar6H6p6rQ2lCpnG6XPYu+m1fKsxrnS9/Woqfv4Ta/LPP/8Cbv2WxaxqAT/0l/5yeMm0Rp7ftj/fatingJf8E8qolAscn59wVQ3d4xz+mk+0vNxd8g3YGZRufO/7+NM991qRDaCLTkBe9HRtLnr8PoemqjL2dJSwq1i6LlMF7DY1iUHr7AXs2fhqvtUYV3rPOZ/yP91jRXZtbS39A/3WIn502cnIumetyC40Fk1p4JVdBel1mDsKeIaa9k6yiHwI+EEh+IjG1M3cfRTjU5hF+8w0Z55F/Ezbh3G+z3Eor22gY98eqhpaRi3il5JizQx2cbyMRIUROdW/fAV1//ppei++JKdyuyLuDZkybQZ/fugRLl154tFjMpCj0NS6WugYQ8mQTNP3Zdlf8TBSpVHEz3EgGPDTH1PCidFJZmpA85dvFvHzGQY8U3cj8srnUSoqqQhgVt9RQX3FHSk1RCvwlIj8REQulIlUE2QYmmfMY9+WDflWY/wQIVZTg3PYzvP03TfcxA+/820rso9S4v6oQxzfXs+re0o0ekoo6Blq2ldV1X8C5gG34uYufUNE/k1E5ljSraARx6G6uZXDeydONv/uy66g/De/tiJ75Wmr2bplsxXZADpjuuvkPwGorwxzsDvbnEWFSmH7oWZ0VXU3ufbE2yBQB/xMRD5vQbeCp2HqbA7s2Dxhsv0Mzp5DwNKgJyIEQyEOHbYzs9KVy5En7OUlKDTKg356B0rUXawUBlQR+YiIPA18HngEWKyqfwmcAlxlSb+CRkSobZ3K4TfTrsJY9AxOm45/6xYrss8693y+9Z3veXccC9OnItu225FdgMxsqGLLgdJM5lPsoadDNAJvU9ULVPWnqhoBUNUYcKkV7UZAUQYGY0ebSSSqo7bBWHKLRGNJzSSqZtOjrbplKgd37xjxuNliRlNIbprcYkbL+t7F82cONROv63Vdcjlld915VD8vYmhSM4nGjrV3XXcjd9/3ELGyGmJlNWP5eCMztOWfy9WEx4wo5Qee5Qwqpe68kQwk8VhLbSV7O/uPZa/3B5JaSh17fyCppVzbOC7+YFIzs+U7AX9SE8c51rKxSuU4Y3/cHvSaiGwQkawz52ViLusArjJsUR3A06pa+mUmR8Dx+YjFonbqzBcgsaYmfAfsOI9XV9cQiUSIRqP4LFSt1NmzYNMWmDMr57ILDfe7WIpbUZKz5byI+ID/Bc7DTXT/lIj8RlXH7LSciWanAB8EpsTbzbgVA28RkU+MVYFSYKiI30RBfT6I2EkXN3/+fO69146Tv552Ks4jj1uRXYjUloc43FN6xqkcLvlXABtUdVO8OOiPgCuy0S2TGWoDsFRVuwBE5J9xy5WsAYb2Vickde3T2fXaCxOmKmr/yUsJPfcM/ctPzbnsD3/oQ3zmM5/hggsuyLls2lth997cyy1QZjVVs2FvB6fMbM63Kjmjs6uL//fZtPPPN4pIoiVyrZGbZAqQuLG+A8jqS53JgDqd5PLOEWCGqvaKSOk9BjPAHwgSHZw4CX77Vq2m+vbbrAyoixYt4s19uSmDMiyOuGG0eUpAPJ7UlIVKMqVfSumYkdmvqstGOT6coKz2STIZUH8IPC4iQ46IlwF3xEs+l36gtAc+f4DBgQH8wQzTohUhWlWNc+SINfkN9fW89tprLGytzLlsPX4BvPwqLFqYc9mFSDjgo3dgkHCJbO9XVlbyyX/8VFp9/+2zn/XqsgOYlvB6KpBVBvlMHPv/FfgAcBjXGPVBVf2Mqnar6nuyUSJbEi3+A4OxFEt6JBZLbobV3yQS06QWM9pwx6ub2jm0Z6fbJ/H6MZKaafXPFDX+87LKx4yWKzQUhP7cL0y6IjHe9q5r+MrXvo46/qSWQqYlL8RBV6/EefjxEazyktSGOz+nfo4efpQp+hhW7FSrf2Jz78mc1jo27j+C+HxJzfPemV4BgWByS7H6j+4l4Av6j7Zs46NTPGNGaGnwFDBPRGaJSBB4F5BVbfO0vhXiMk1Vn1bVL6vql1R14nhJp0FVQzOdBydGGQqA/lOWE173lBXZF19yGS++YKnWVH0dHDg4YcJQm6rK2HO4u2SCT5RU176Rmqcs1UHgQ8DdwCvAT4ap8JwRaQ2o8QipX2VzoVJHHAeNu09NBPpWrCT0xGNWZAcCARzHR3e3nVpWesLx8NLEyBYmIsxorGbz/tJx8jd9qUdqacq6U1Xnq+ocVfXcI/Aik3XL4yKyPNsLljIVtY10H5oY7lNaUYHT12tN/spVp3HHL+zkDdALz8W5217+1UJjbkstG/cdKYmHfS5nqDbIZEA9C3dQ3Sgi60XkBRGxVwO4CGmYOosDO7fkW41xQ30+sFRW+8b3vZ9f/u4uK7IpK4OBCESj3n1LABFhfksNr+8dQwrDQiMlanHklg8ysfJfZE2LDInFoGcgkx9DctRNICXfqSnfyJ9q5CQ1t9yjQ1X+fD6i0SiD0djRQmRmkTzH8NSIGtfyGflRM92+NychXvv/5qwlk2ivgYWLCL70IgMnLUn7HC96Iq4+dc3tdPb0oQkhqNKduyoJevoq5OHH0beszpnMFFLynRqHvc7PtIhfQiE+MUIvZzTVcs9L25jXVo8zZOBKxHgtxsiQci3zYWSeH0j2dkm8E9na8wp5pp3JR9sGnAHcoKpbce9xixWtipj6KTPYt31jvtUYF/pXrCT0pL3Io+amJl5+5RUrsvW0FchjT1qRXaic0F7PyzsP5luNrFBSPVdGavkgkwH1a8Aq4Jr4607cONhJEqhqbKVz/97kDOUlSrSlBd8+e54N737X1Xxj7a12hPt8EA5BdwnXXzJor6tkz5EeokX+3TQTCI3U8kEmA+qpqvrXQB9AvBRK6Xuxj4Hmmcexd/Pr+VZjfLCYEOaySy5i/YsvWpMfu/winF/+zpr8QuTEqQ28uKO4Z6mlYpSKxLOzKICINJG/mXVBU9XQRHfHQWLREk3wm8Bg2xR8O3dYke33+wmFQhw4aGkAmD0LNm9zN+UnCM3V5Rzq6cu3GmNGPdJjZhM0kwsyGVC/AvwSaBGRzwIPA2lnKcglMVV6BwaPtp6BaFIzI6fMG50aOWU0MxrKI1+qGZkViymts49n1+svD3MsOXLKPB6NJTev/Khm5JRJtssgr6d+32mnE37ggZzlR02k3wlz+Vuv4otfu4V+x6MK3FgQBz3zDOTBR3MTOWU5kspTn6Top+Ejp3AcQoEA/TFNjp4yI528orJGvbaTGj2VEGWV7b0p5CV/2lZ+Vf1BPGP/OfG3rlRVOxaDEqCsupa9m18r+fj+yLz5VP7QUpZ94NrrrueKy3JbaTURPWMVzue/hJ61xto1Co3Zza6j//FtdflWJWOkq5Pq/8ja/94amZSRDgFLgZr4ee8QEVT1M7aUK3ba5i1ix+svMHPRKflWxSqReccRePUVIguOz7nscDiM3++no6ODMhsJokSOlUqYIDRXl/PKjv1FOaDGKqs49Il/TK/zf37OrjLDkMlX9Ne4yVcHge6ENskIhMor0JgyYDGiqBDovuKtVPz6F9bkX3TxJdyy9hvW5Ou82bBxkzX5hcaQr3Eh+3OORqkYpaaq6tWq+nlV/e+hZk2zEqF19nz2bSvtH6tWVIAIYskF6drrruf+++63IhtAly9FnnrGmvxCpK2mnN0ddnIl2KYk9lCBR0Vksaq+YEORuAfBOmCnqo5a9E+VpOJ8Qb9pKEreNDcL+flSNvRHt/I6xiZ6xHj8OUZ0U6LVOFBWRU/nkaNpAh0j6srx+MMrmUVOmU/mFNWM/uYTdayRU91vewcVv/gZ3dffkFb/dOiKR8P5y6vo7esjFqhI0kcGupJPMNPQpWu9nzYV2b7TfgWmTCOndHT9zcgpYsdep8g2DEmzmmt5avNe2uur3TfMSCcn+drqJEdGSSA1RV9Sf/ONWG7CfDWPFvx0yGSGejrwTLxCoI1Y/o/iptAqOXzBIIMDpV3UIDJ3HoGNb1ibGkybPp1Hn7AU2TQBiiuaBP2+YT1WioFSWfJfBMwFzsfN1n9p/P9ZIyJTgUuAb+VCXqFR3z6Dgzu35lsN6/QvP5XQU3YGvXdfdyO3fvcHVmQDUFcLBw/Zk1+AVIWDdPQW14N+yH5YqEv+Qonl/xLwCUo0UKC8pp6ejuKOTkmHngsuouyPf7Aie+Xq09m0ebMV2QC6bOLto85tqWFDEWagMn2ZR2r5IO+x/CJyKfCmqj7t0e9mEVknIut6jxTXTEJEwHUxy7cqdvH70bIypDP3yYwdxyEQCNBhqZaVLl6IvDCxSqMVaxG/Qp6hZmKUOlVVl4rIs+DG8sfrsGTLauByEbkYCAPVIvJ9Vb02sVO8/OtagOY5JyTdLjMFXv/g6BNdM0We+VzxSu9n7j2liPMdEzC0dR+qqKS/p4tgdXWy7JhxsmGlMrf+zS9Kijkuw+1ALyNVSn8Po1f3RZdSdvdddF/1TiC77Unzz3jO+Rdy6x0/52Mf+xgAPtMoZZKJkcrvz5nh5ChmRJCHkcnzfBMdOYWeedvVMDIRNzKFgn76ojHCXqkBB42B1zRi+Qz5Ma9Ug2NDlWHrwBUKeY/lV9VPqupUVZ2JWyTrz+ZgWgpUNbTQeaD0a8JHFhxP4PXXrMh+7003cfcf/2hFNuDWm9qfu5yrxcDcllo27j2cbzXSRhkm1HuElg0i8g4ReUlEYiIyWinqJMYSy9+c71j+YqSipp6ew6W/j4oIYmm91djYSF9fHzFLyUxip5064XKkNlWVsftwMfmjppcYJQeuVS8CbwMezOSkTMpI/wDXcPTvwG7cWP6fZnKxNK5xv5cParxnLi87LojjlP4eapzB5hZ8e/ZYkT1z5kwefOghK7JZMB95xc7sulAREabUV7LtoMf2SYEwXjNUVX1FVTP+MmSyh4qqvgrkvVykM1hcrh5DlFXVcGT/XqobS7vQQd/pawg/+hDdb3tHzmV/+EMf4vOf/zxnvuUtOZeN4xTjszprFrTVce8LW5hWV5FRCZx8MDgQ4Y1nn0i3e6OIJJa7Xxu3xVgjowG1YBDB33OQwfL6fGuSEc2zjmPLMw9TVd90tOZUKRI5bgGVv/iplUQPS5cuZc9ei3vRU9pg5y6Y0m7vGgWGiDCzoYotBzqZ1VjtfUIe8QUCTFucdvHl/ao64v6niNwLtA5z6FOqOqaSu0U5oA46QXwHt9Mp5agvNQTOtPpHg8kf07TyB33Jg5u5/5Jq1R89FNWXEF4aNaz2LXOOZ+eGV2mbt9CVZVwr2yJ+Zk7UmCb3MIdxKxOSxAxOHhcw/QXNz59IV8T9O1TV1PDC6xs4qa0q+bKxLBN6i0Ps9NPc4n3venuKVd7MQZqyV5ylVV+N88Xj/BR9Eiz16uGxYIaizm2t496XtzOzqdadpRpF+MQ/ukNPysR+tPOz+NK5S/4xn54sS/Xc3Eg6RtFOkzoa5lFz0F6ooy0q65vo6z5S+qGoc+bg32SnWOE7rr6GW77+dSuymTkd2bTFjuwCRkSY3VTDpn12/HxzhpKShH2klg/SHlDF5VoR+f/ir6eLyAp7qo1OzBeir6KZik475TdsMmXBiex4qa66vgAAIABJREFUNZdpEAqP3jVnUvbg/VZkX3bFlTz//LNWZCMCDQ2wb78d+QXM7KZqNu3rKGjjqZKeQSoHblNvFZEduMFMvxeRu9M5L++RUtnQV96Ib7APX39xWCiHCIbLCQRD9BwpHv+/TIm2T8G/e5cV2YFAAEccenrs5JmNXXYRzm/utCK7kBER5rbUsuHNwg5HjWp6LRtU9Zdx//iQqrao6gXpnFf0VU+P1M2h7MDGolv6t807gd1vvJRvNayiItb+LqeuWsUdv/iVFdm0t8Ke0g/CGI6ZDVXsKGAXKjdSapg6cMO0fJCJUapgqp7GYkpX3zEDhK9iGuG9GzhSOwuAqnDyxzLzpZr7KwNeN99vPHc88qsmGqJMg9WxWyZUNLTw5q7t1LVOHVFWzMifat5xMYxUKaGk5vmeGVWTGWt+VIDBWbPxb97E4Ow5GV1zJHoix3S55oYP8H8//le89wMfPKZbd+6inHTuHHh9A8yfmzOZKXgZsTxDT0f53pqF9ByPsNr4cQECAR+DCIEEY22KkcsjfypmrlZ/gvE4a6NU4U6eirLqqclAqBrRGH6v2O4Co37qLA7s2IyWaBnjvlNXEX7icSuy26ZM4YiFJCxD6CXn49x1jzX5hcz0huqCdvQfjyX/WBlLpNS/AbuwECmVDR21s6jp2FJUS38RoXHaHA7sKs1cqYMzZ+HfYq/8S2tLC+tfeNGO8MpK6OlNP+t/CTG1rpKdhwqzXNx4RUqNlUys/B8HLgZC8XaRiLxPRJbYUi4jxKGzejpVR7blW5OMqGluo3N/ie7XWdxDBbj+PdfwjVvs5STXU5chj6/z7lhiOI4U7rJalVgsvZYPMlnyLwM+CEyJt5uBM4FbROQTuVctcwZC1fgH+/KtRkaISMGH+2VDtLUNnyVr/4UXnM/Lr9iLhNY1pyEPPWpNfiFTUxbkcE/h+Uorrl0inZYPMjFKNQBLVbULQET+GfgZsAZ4Gvh87tUbHiXVsJRIitHJMCL1DIy+QR/1j/7HCASTN9xTIquSrm8asEZ/hpmyrBfxM4+b6VkzlWec37PmLML33EPXdTe4+nkomBg5NVrUFMBgoIJguIydBzppbGqizEPXjAkEIRCAvgEoC2cfOWWSbX7UlKJ/x+SNVsDP7WuKSi3it3nfEU6uLHff8CriRyRZoBlanXR+dkapUinSNx1IzDIbAWaoai9QMI+ymC+AmMlwCxxxfMSiOU5uXCAMzplLcMPr1pb+V1z5Vr75DUtRU0Ds/LORP/7JmvxCpbY8VJj1ppSSWfL/EHhcRP45Pjt9BLhDRCqAgqkd0ReuI9hbXEmCh7L5lyq9q08n/OgjVmRfe931PPRgRikrM2PRQuTV1+3JL2AckbwNTCPhzlBLw8r/r8AHgMNAB/BBVf2Mqnar6ntsKZgp/aEaAr2FHelhEi6voq/bngtQvuk99wLK7k0rci9jwuEwPr+PgwcsPURLeH/bi7baCnZ1FJ61vySs/HE2AY8BzwDlIrIm9ypliTjEHD/+3uIJ66ysb+Lwnp35VsMejuMap/bstiL+kksu5dZbLVYgb4+n9JtgzGioYuv+wkqWoqoMRGNptXyQtlFKRN4PfBSYCjwHrMQdXM+2o9rIxFTpHc2wVDWT+gOv0BPzMRgoz1h+ahG/ZFIsiKMWBTQKAJqBTzHF8QcIhMvoOnSQyrrRc7z6/KPrlvJgLpAJVveVV1Hxy5/S+ZcfypnMrvh34Iqr3837r3s3//cjNycdl4hR2iOTon2J3VYtRx5/Cn3bZWPWdSx4pvMbzeg1SgE/GC7tY6pRKRh03O+646QYuVIip/zJaTRH+9pl49XiZZDON5nMUD8KLAe2qupZwMnAPitaZYsIBxuOo+bwRiQa8e5fALTOWciejQWzFZ1zoi0t+N5804pxqqamloGBAXtZkubMRjbYC1AoZOorwhzospOEZixoqaTvA/pUtQ9ARELxcijH2VErB4iPQ/XHUX/wtczdU/KA4/NR1dBCx5ulu7QcOHEJweefsyJ76rRpPPrkU1ZkT+R91Lmttbyxp7C2z0plQN0hIrXAr4B7ROTXuCGoBUvMF6SjZhaV+4rDSts4fQ77tm0s6HyU2dBz4cWU/8FOWrxrrruB2777AyuyAWhugr1v2pNfoJQHA/T0RwrG2q+kN5gW9IAq7qbHR1T1sKp+Gvi/wK3AlRZ1ywmDwQoi4WqCXYW5O5GIiNA0fQ5vbnkj36pYQcNhd83Wn3v/xtNOX8PGzVtyLneI2KoVyKMTq8T0EAva63l596F8qwG4X5+BwVhaLR+kNaCqO2X6VcLrB1T1N6paFB70/VVtBLsLf0AFqGlup6fjIJG+wtm3yiU9F11C+V2/z7lcx3Hw+/10dlry5z1uHvJaaT7ovGivq2TPkZ6CiO8vpT3Ux0Uk7XKDNonFoLd/8Gjr6hu99URiDKjQ2zdAz0DU88abx013jP7B5GbGEA8mNK9ojkgsltRiMaV9wUlse/lZt4/hWxc1m6GrQlKLaXJT4z/zeMq9NpqqJrWU/h7y+k9cQvDFF1HN3j41GEtua846h+/+/LfEymqIldVkJ9xExG2jeAaoSFLzlumM3rIgY10cX1IToy1ob+DVvUdS+o3UH8cxWkL/LCmVAfUs4DER2Sgi60XkBREpmsJIPeUtlPcUR1anQDBMVX0zh3Zvz7cquUcEdVKrauaC97/3Jn53p73SJbp4IfJC6XpijMbUukp2He7O+/7+eO2hish/isir8bHul3H7kSeZDKgXAXNw/U4vAy6N/78oGAhVE+wrngiqhmmzObRrG9FIcbh9ZUL/KcsIrcu9Rb61tZWeHns/ej3jNOShx6zILgbmt9Tyep4t/qokrQBHa1lyD7BIVU8EXgc+mc5JmYSebgVqcQfRy4Da+HvFgQgxXwAnWoAJH4ZBRJhy/Elsf61oFgFp03f6Wyh72E78fXt7O089ZSmHaVUldJVuzgUvpjdUse1gZ15nqeNllFLVP6rqUJ2lx3EDmjzJJFLqo7ix/L+Iv/V9EVmrql/NSNM80lk9nZrDm9CqxflWJS1C5ZXEooPeHYsMLS9H+u3krf34xz7Gf/33F7jjh5ZcqKoq4UgnVFfZkV/gzG+p5Y29h5nXUJGX60ssQtnetLddGkUk8em6VlXXjuGy7wV+nE7HTPKhvg+38mk3gIj8B27oaR4GVEVHmdKPnA/VT8BXiX//TvoqW0bpn7xx3j+YfJvMIoCOsekfSEhiGjUSmnonvh39yWraRCKpZfmSXnkV8fMZ9oqUlJ6WfNo1FEL6+tw8oyMQM7J2euVH7YkKxy9Zxs49e+iJClVeRfAyDUUVB111KvLE0+j5Z2efz9Tr/JR8p8bh0c41ZKeEjpr90yziN6O5lvte3s785mrjcqOHumKGto6RmOOno3FBut33q+qykQ6KyL1A6zCHPqWqv473+RQwCKT1hM5kD1WAxLsWpWAixdOns7KdYP8RAkW0n5pvQ4AN+pcstRY1VVtTy5Ytm63I1pMWIc8+b0V2sRAK+OiP5Cd/by7dplT1XFVdNEwbGkxvwLUVvUfT/BFmMqB+G3hCRD4tIp/G3Ve4LYPzC4Yj9XOp6NyBz0yeUYD4AyEGI0Xh7psRfctXEH7qCSuy3/Gua1j79a9ZkY3f785s+4pjL94Gs5pq2Lw/f+kmx8nKfyHw98Dlqpr2QJGJUeoLwE3AQeAQcJP+/+2de5RcdZXvP7teXVVd/e7OOyHhkRDyICQhJAEdRFBkJIA6F7gQvAtmGL16R6+67h1u1nK4Oi5QRGe8zpVBYbgo4qg8BBwFRHnIQ8yj8yLhFRKSkJBHdzr97q6qff84p5OqU91dVd3nnKpKfp+1zkrO67d/9dr9O7/929+t+t1iO1oWiHCk6Uzq2spfPKUqXn1Cik9rbR2Bo948Jay64kpaN2zwpG0AvfhDyDPPetZ+uTOpLs7+o6UZjKgqA8lUQds4+T5Qg5Vm3yoidxVyUzFVT7+pqutV9Xuq+s+qusGeR61MAkGONM+h/vDrZV16uq5lEm3vnYDrUQENBDxZjxoOh5GA0NvrTeBLFy1ANm72pO1KwCosWRrbQ/J9Xo9QVfV0VZ2uqovs7TOF3FdMUOoSrCFwJh8b5pjnqMLgKHM4hRfxCpKOTqT68Lt01x5fFeHUQ404ivY5P6xBRzCjfxQf4fycq0LZf9MCjsn8cDTBQH8/Pb29hCNRnGGlsONvYlqyDeQr4qeO8ERas6/I+YubE8TKvr8YrcuBhYuIbNrIwDmLC75nNLoGjr83S5at4IHHnuTGT99w7Fig16V8dBGor4P2dmhocKdNKD5oVURbmifY6SzSp47vtPN8vCpC92Ca6ipbBzVPEb+s8+PRQ7XXoZYreUeoIvJZEdkMzLGzBoaypN4Bxr1IUkSmi8gfRGSbiGy1l2f5Rm+0kXD/USRdvsuTJp8xn31vbCl1N1ynb+X5RF/8oydtr77xZh5+9DFP2gZIX/4xAo//1rP2y53J9dXsO+J/eRS/RqhjpZAR6k+B3wC3AX+fcbxTVdtc6EMS+LKqrheRGmCdiDytqr7l+HXWzyRxZBedjaf5ZbIoIrE4IsJAbw/hRGnW/3lBur6BQIc3mTeTp0yhq9vKmhqPQvyITJ8Ge8pavdJTJtbG+dOO/Zw+saCMTPfQylfsn40lLn2tnRn1F8D3gFtFZPR6HQWgqvtUdb39/05gGzB1vO0WQyocJ6ApAklv5tzcYPIZ89n35ok3StVw2BM5P4ApkyezodW7JU46cwbs2OlZ++VMOBgoiWNTtOLl+/4VGACwi/LdDtyPVfl0LFkHIyIiM7FKq+SspxGRm0VkrYisHex2f1RztH4WiY53XW/XLUKRKrvu1KFSd8VV+pee60leP8CNn76eH/zwHk/aBtC//CiB/3jKs/bLHQGSPhfDK3f5vkIe+YMZj/ZXY6VvPQQ8JCKurcwWkQTwEPBFVc0ptWinjN0NUD1ltg70Hp/zHC1rCqBXnEGm3L8jGgwjdoAlN3MqO8rUMzD2R8h8BQCdyuiDcvwL23zqXN5tfZlZi5YTPFYUzRGkcmTEOF+LM3Oq2NSM0fOyhrne8dE4zfetuIC6f/o2fSsvsLozSn+KzZz6wCWX8bVv3smgXaixyq2g1BD19dDZBdjSfo4gklM2T3LS0FwMQjnby9NW3qJ7OVlkuedPn9TAmwePMndKY3FF/MY5BZPv915KChmhBkVkyPF+GPh9xrliVgmMiIiEsZzpA6r6cL7rvaN8PygACQSYMW8xu7euK3VXXEOjURBBetxf1xgIBIjFYhw84F3pEl2yCFnr3ZrXcqYUgSlV8moMD22loBCH+iDwnF1Dqhd4AUBETsd67B8XdnmVe4BtdvJA6RhmlFFuRGLVJBoncHjvzlJ3xTW6V11F9WOPeNL2FVdeyV13FbQme0zohz6IPP+iZ+2XMyJCoipMZ5+fmXyaI3I+0lYK8jpUVf0G8GXgPuCCjJzWAPDfXOjD+cBq4CI7I6FVRC5zod2iSYbjhAbLv/RI8/RTOXpwHwN95Z86WwiDc88i/Pp2T9q+7rrreeH55zxpG4BIBJLlu+TOa86a2shrew/7Z1AhlUwXtJWCgh7ZVfWVYY65UkpUVf9ImYisDIYTVPUeRhO1+S8uMdPnLWH3lrXMXnp+qbviCoNzziS8fRvJuXNdbTcejxMKhzl06BBTI642fZzaGkvSr+bEWdJWKIlohO7+JOm0EsgTH3ADpbwfIl2ZA/UbVSWZkSmVr/yOcz7FmUnVO2C3pTEa+45Qs2crbXWnomJNtMci2RPu9fHRHycyA09hx+T+YMoRWJHRvx0pR+bSsU8sGEIDwZx1ls72ww4VNWfmVG7QaPTMKafcX06QaoyZU90fX0Xt3Xdx1EWHOpQ5ddElH+Xue/6NW//ur7P75hTHKVbOz0aXLkb+vB696ANj7uuw5Pli58j5ZXqafD8KHV1uLyejzpH5lHl+7tRmtr3fwbypTSO3l3X/OINSZZwqPr5KYCcaIrTFpnC0eioT2rYTGShvUZKq6hr6unIWRFQkmqgh0N3tia7CNdffwAvP/j7/hWNEz55/Uuf2T2lIsK+jxx9HdwIEpU46BsNx3m+cS6L3AHWdu8tWPCVe30jXETeS1cqDgfkLiGxx3zHV1zfQ19fn3Q8+EoETsPZXMcxsqmHXYT8k/Sxx+UK2UmAc6khIgLa6UxkIV1NXpopUsdoGujtOHIfac8lHiT3lTX781GnTeXXdek/aBiAeh27/c9vLhdMm1PH2Ae9F21UhlUoXtJUC41Dz0BttpCcxmfpD28vOqQZDYdInUIRZEwlrPaoXj/3Xreae+z2qM4W9HnWdNxUIKgERobkmxgEfdFLNCLXCGYzW0R9roKrP5UwbF1BN50itVTKpSZMIHHZ/Gc4HL/wQb7z1tuvtDqHnnI1sOPEq1BbDvCmNbN3r/RNTOTvUyozyp5X+3sGs/ezzo98fdKSepoKO/WE+jP5oA9Wde+mPNeYILxSTmppWR2qoOqPq2fvByOgfUf3kU3hvx5tMmDV7qAXHFQ69VUcU3+8ifvlSUdN19QSOdpBubs7fVp5U1IGsFQ9CKBKlMx2iutpa3uRqCZxYNK/IS95UVB/J6UuxRfyGSUUNBQJURyMc6RukPpInFXWMqJYu4FQIZoRaIOlQFYFU+dV2qm2ZTG9XB71dlVN0cDTSdXUEOrx5LStXrODBn/3Mk7YBK1/9JA9OnX1KC5vePeipDT8ypUTk67b2c6uIPCUiUwq5zzjUE4DpZy1m77aNpD0oJ+I3aQ9rTd1000089vjjnrQNoAvOQrZs86z9SqAqFCTtYeqnKqSSWtA2Tu5Q1YWqugh4AvhqITcZh1oEGgiWpbJ/IBhk6tyz2fNa5Yt0pOvqCBz1Zm3t9OnT6Or0bm2xLl2ErPVwJUGF0FIT52Cnd9rCfsyhOhTvqilQOck41CLoizVT076j7KL9ALFEHbHaeg5VuGhKqmUCwff2etb+hIkT2OBVRdSGBmjzpgJBJTGrpZadXq1J1aIcavOQhrK93VyMKRH5hojsBq6jwBFqZQalFJKDx8Mn4pwwd16fE7TK3k86Cv71Ofa7+oZGpTFi2si0d1s5VH8G6aCVHO4MUiWix/dT6XDWOaceatgZ9XEw6FhP1+e8POMTrJ9+Kns3vUqsrplIzAq85KaSjl7Ez/l3WPOkorpdxC/V3EJw//uojls2M4eelPC5L3yJ2++4k3/78QPU5NMjLTYVVQIQjUJfP8RixSedF6uPmlOIL+NUcZZzU1FzTDmCTM4ifBlBp3hVmL5kOjv91KUifaA53+FROKSqS0c6KSK/AyYNc2qNqv5KVdcAa0TkFuDzwD/kM2hGqEXSG0pwqGE2zUfeomqg/NI+p89fwp7t3pX98IPkrFmEdnizxGnJ0nPZv3+/J20D6F9cgDzrTeFBw5A4ijuP/Kp6sarOH2b7lePSnwKfLKR/xqGOgXQgzIHGuVT3HKSme1+pu5NFMBSmtnkS7fvKt5xLProvv4Lqx53fafeora1l5853PGlbz56PrD95F/h7jk/yfSJyRsbuKqAgfUnjUMeKCG31VpXU6nZvfpxjpWnaLNree5d0qvwCaIVgVUPt8Gyu+lNXX83dd/3Ak7YJBGDqFNi125v2DX4JTN8uIltEZBPwEaCg8vbGoY6TzurJJMNx4kd2lrorxxARpsxZwL43t5a6K2Om/9xlVL2aU6vRFa686pO0rvcuGp/+1BUEHvZuhF0JREJB+gbd/4Ou6o84iqp+0n78X6iql6tqQZHSygxKpdMM9PRl7Fc5zo/+ZgZCjsBK2jG5n+d+ZxDqaLSFuva3SPb1kApFCQ4cn6DPV5TP2dZgVbbtKkdWVzydHRxwdjVux8BC8Rr6+/tyJvCdeqm5Rf6y7TnbzxNDc62IX8/FH6Hhtn+kb9nyY+fGG6Qa0keFIIrQrRFisdhx224V8UskYGDQUvIPjfwTKzpzyu2ifqP0JS+OgJ0zaDV7Uj1b97axZOYE67wjE2s8mEypk4DO2lOo7dhV6m7kULF5/uEwqZYWgvu9maNetmIlD/78F560DaArlyMvv+pZ++VOY3WUjp5+T5yfplMFbaXAOFSXSAfDpAMhQm7mh4+TWE0dPZ2Vm5LadfW1JB70RiHqhptu5qFHvHss15XLTmqHCjBncgPb97srKKSaJp0cKGgrBcahusjRulOoOVo+0fV4QzNd7YdK3Y0xk25sItDbg/S6Xzhx4qTJdPd4qDIfDFqP6APlp//gF1MbErx3pNvd91hBU6mCtlJgHKqLaCBEKhgl2O+Hcnl+YjX19HSUn+RgMXR94q88KzE9Y8Z0Xnn1z560DaAXXoA8d3KWmB7iFNeV/LWsH/krMiiVTiXpbX//2P5gLLvaZDiayNofjGYHrQYdmVCRquz9wars6ORAVfbb1BfLzn7KDCy1Bydx6oE3abczqYaTAszEGbSKhJxBImdRPWeQafRMqv7BJO0dHVTFrffE8VJyigAGnIESR//yZU7lK+JXbGhi8My5JB7yZq7zxr/5DD+49x6WXnAhAFVuBaVsdOliArfdiX7kw/YBjzOnMi913FtsPC+fnF++In5Dcn+nT2rg96/tZmZzRiXh8UQXVUvmLAvBjFBdRiXAwYbZtBx5oyyEVKbNW8L+NzbTdfhAqbsyZjQg4MEj3Mrzz2fXzp2ut3uMQACam+GAt3J25YyI0JiIcrjLPbGUch6hGofqAelAmEP1ZzCh/fVcIV6fCYRCzDh7OZ2H9nNo11sl7ctYGThnCVWt7q8bFRFisRgHD3j3xyb9icsJPPprz9qvBOZPbWLLXneqMGiZP/Ibh+oRqWAVh+tOpe7gtpKrU4kIk+csJBAKsWvL2opbStV7/gVE//iCJ22vuuIK7vrXuzxpG4CWZjhycitQhUNBggGhd8CFJzZVUsmBgrZSYByqhyRDMbrrZ5BoK4+RYePUmTRPm8WODS+RLuN5KCdaU0ugyxsd0+uuX83zzz7rSdvHEG+mLCqJhdOa2bzHhRUnakaoJzXJqlpSoRjxI7tKPlIFqK5vYsrsBezeWlli1OlEAul0X90rHo8TDkc47EFhwCF0zhmw/U3P2q8EamMRuvqT415CZalNla9Drcwof3KQ3vbjEmzhvuyofjKaXR/duQogOVCXfT6aHbUPV2VHOJ2rAFKDzlUB2W9jd//x/a6+ENBMYqCdht0beb9lzjEdVYCaaPZjUFc0u636eCRrP+E4Hw9n99WZmuokIEIoXkMymWQwlSbsSBkczJkOcP7NzY7QhpyB6DxF/IrVRx2ib/lKoq+8TO8lHx3xmnxF+5wMpaJ+8KIPc+9997Pmszdk9y3pCKQUq49qo8vPJfD4b0ifNTv/xcXgXAWQZXR80zp5i/jlcVg5+qnpNNMaEuxt7x7hjkI7piVbY1oIZoTqE12RBt5LnEbDkbeJ95Q+6hsMR0gOjF6ls5zoP2cxkQ3eCJpcu/rT/OGZpz1pG4AJLXDIuxFwpXBqSy07Do4/c6+cR6jGofpIKhDmcNNcAulBGtvfQEo4j1nTNKGyllJFIohHFUWbmprp7e31LmsKrNFthQUD3SYUDBSjtj88JvXU4KQrMYWjiWk0t20j1Fca1f/qxha62irIoQLpmloCHd5EzCdPnsL6jZs8aRtA586Bba971n6lML2xZlz3W3Oo6YK2UmAcaolIhuMcbJpHpPsAsbZ3fA9YBUNhBvv7KmoJVf/KlVS99JInbV9z/Wruvu8nnrQNoCuWETjJxVIAZjaPz6GWe5S/IoNSmhqk78jx1NNkX/aSmpAj9dQZtEoNZAcbktHsoNVgTupqdtDKWdQvXOVMZT0eaHIGrHqdBQCrphEfOELDu628nziVzursvvQOZF/vDErVOPrmPN+fzN7vCR9vLzLldDa+/BwtZyygKmGlBjqDXDWR7PuDEWdKortF/NIZFzilZPsWnkP9d75Fz6WXWU3l1WYdPUg1kKENu+KDF/Ev3/kW6djxgGXQzVLITY3QPvroumh9VDcZr9aqM2CXc9763gXzXVcA5Zx6WpEO9USjJ1JPXyjBxK4dpKSJnvhEX+xW1TZQt/A8Dr39GppO03L6fAi7JwTsOuEw4lGENxgMEggE6e3tzRKddh0vyrmeTKiW9RrqsnjkF5FLReR1EXlLRP6+1P0pBelAiH21swmk0zS2ve5bwCoQDDFh9kIaZpzO/tfWcujdt70NzoyTdKIGOeqNxut5y87lF798yJO2AXTWTNix07P2TwZUlfTgQEGbG4jIV0RERaS5kOtL7lBFJAj8C/Ax4CzgWhE5q7S9Kh1dickcrZ1Bc9s2Iv3+iUNH4gmmLFxOuCrKrtZXynZute+8FUT/9Ionbd900408/Ig3UoEAet5S5NV1nrV/cuDfHKqITAcuAQoWOS65QwWWAW+p6g5VHQB+BlxR4j6VlGQoxsGmecT62ogeftvXgFXdxKlMPO1Mdra+TLoMF1D3L15C9GVvAlOzZs6ko8PDVRfTpiB7Cqr1ZhgFH4NS3wX+B7nqhSMipX68E5FPAZeq6l/b+6uB81T1847rbgZutnfnA1t87ahFM1AKCXxj19g9kezOUdUxhftF5LdY/S6EKJAZWbxbVe8u0M4q4MOq+gUR2QksVdW871U5BKWGm6HP8fL2G3E3gIisVdWlXnfMibFr7Bq77tgd672qeqmL/fgdMGmYU2uA/wV8pNg2y8Gh7gGmZ+xPA94rUV8MBsNJgqpePNxxEVkAzAI22loT04D1IrJMVfcPd88Q5eBQ/wycISKzgL3ANcB/Lm2XDAbDyYqqbgYmDO1X1CO/qiZF5PPAk1glh+5V1a15bitoHsQDjF1j19itXLueU/KglMFgMJwolMOyKYPBYDghMA7VYDAYXKLiHWqxqWEu2PueRXW5AAAIaElEQVS6iGwSkVYReUpEpvhk9w4R2W7bfkRE6n2y+1cislVE0iLi+RKbUqQhi8i9InJARHxd2ywi00XkDyKyzX6Pv+CT3aiIvCoiG227/9sPuxn2gyKyQUSe8NOuH1S0Qx1LapgL3KGqC1V1EfAE8FWf7D4NzFfVhcAbwC0+2d0CfAJ43mtDJUxDvg9wbX1jESSBL6vqXGA58DmfXm8/cJGqng0sAi4VkeU+2B3iC8A2H+35RkU7VMaQGjZeVDUzN7HaL9uq+pSqDukCvoK1Ns4Pu9tU1S9l5JKkIavq80Cb13aGsbtPVdfb/+/EcjJTfbCrqjqkeRm2N1++xyIyDfhL4Ed+2PObinWodmrYXlXdWALb3xCR3cB1+DdCzeRG4DclsOs1U4HdGft78MHBlAMiMhM4B/iTT/aCItIKHACeVlVf7AL/hDUIKk/1nXFS8nWoo+FFath47arqr1R1DbBGRG4BPg/8gx927WvWYD0qPuCGzULt+kRBacgnGiKSAB4Cvuh4AvIMVU0Bi+y5+EdEZL6qejqHLCIfBw6o6joRudBLW6WirB2qF6lh47E7DD8Ffo1LDjWfXRH5NPBxLNEG1xxNEa/Xa066NGQRCWM50wdU9WG/7avqERF5FmsO2eug3PnAKhG5DEu4pFZEfqKq13ts1zcq8pFfVTer6gRVnamqM7F+iIvdcKb5EJEzMnZXAdu9tmnbvRT4n8AqVe3xw2YJOJaGLCIRrDTkx0rcJ88QazRwD7BNVb/jo92WoVUiIhIDLsaH77Gq3qKq0+zf7DXA708kZwoV6lBLzO0iskVENmFNOfiy1AX4PlADPG0v2brLD6MicpWI7AFWAL8WkSe9smUH3YbSkLcBPy8gDXnciMiDwMvAHBHZIyI3eW3T5nxgNXCR/Zm22qM3r5kM/MH+Dv8Zaw71hFvCVApM6qnBYDC4hBmhGgwGg0sYh2owGAwuYRyqwWAwuIRxqAaDweASxqEaDAaDSxiHajAYDC5hHKrBYDC4hHGoHiMiKXvB9hYR+YWIxMfR1q0i8pUx3FcvIv/VceylsfZjLHhpb7jXN8w1MwvVOxWRvxWR/fbntkNE/otL/YyJyHO2TCG2ju+PM86HROSgiDwhIt8VkS9mnHtSRH6UsX+niHxJRCIi8ryIlHUa+cmCcaje06uqi1R1PjAAfCbzpFh4/TnUA1kOR1VXemwzC4/t5by+cbIQuNXWvP0UcKdL7d4IPGwLkwB0A/Pt9E+wtH332v9/CVgJYH8/moF5GW2tBF60ZQ6fAa52qY+GcWAcqr+8AJxuj5a2icj/BdYD0+3RxhZ7yxyZrLEV7H8HzLGPZY22xKpacKv9/xvEUvXfmDH6uR04zR5x3WFf15Vxf47tjD7+UCxV96cyfvhkXLNdRH5k3/uAiFwsIi+KyJsisizj2i7732oR+bXdvy0icnWR7TwqIuvsPt08yusb7n0IjvZ6MljAcQHkPVjVeN3gOsCp3vUbLH1QgGuBB+3/v4jtULEc6RagU0QaRKQKmAtssM8/ardtKDWqajYPN6DL/jeE9WP6LDATSw9yuX1uCbAZS7A6AWzF0sYcOh4HaoG3gK/Y92/JsPEV4FasH97rQLN9vNH+N+t6R79Gsj0TSyZwkX3dz4HrHW0MXbMA64/zOuBeLBm+K4BHh7H3SeCHGcfrimxn6DXFsJxM0zDvR877UMjrybi/HZho2/9H4CcufA8iwH7nZ4A1Gv4llvpSK3Ah8IR9ficwA/hbrCebrwOXYWkAPJ/RThA4WOrvutnUjFB9ICaWkO9arFIt99jHd6nqK/b/LwAeUdVutZTUHwY+YG+PqGqPWjqZ+ZSXLgJ+qaqHAFS1EBX6kWwDvKOqrfb/12E5JSfvqKX+lcZyxs+o9SvfPML1m4GLReSbIvIBVe0osp2/E5GNWFULpgOZ6l9DjPQ+5H09YpXVSWAJtLwKNACfG8aG876v57mkGTjiPKiqm+x+XAv8h+P00Ch1JZZ4y8sZ+y9ltJECBkSkJl8/Dd5iJrK9p1etubhjiKXh2p15aJT7h1OvSZI9XRPNaKdYtZvRbPdn/D+FNSoc7Zp0xn6aYb5fqvqGiCzBGmndJiJPAfcX0o5YosQXAytUtUcsHc8ouYz0PhTyehZiOfOsGlMiMgn4dyz923lYDu0SrCeDQ0DIdsZfBTqA36rq7zKa6B2hr2D9ofw21ui0KeP40DzqAqzR+G7gy8BRrBF8JlVA3wjtG3zCjFDLg+eBK0UkLiLVwFVY863PA1fZ0eEa4HL7+veBCSLSZM+nfdw+/gzwn0SkCUBEGu3jnVjSf8XY9gSxqsT2qOpPsJzI4iJurwPabWd6JlZhO8h9fSO9D4WwABiurM45WAGlb9n9+CHwC+AU+1wrcCZW4PF7DmeKqrZjzeEO51TvBb6mqpsdx1/E+mzbVDVlj7TrsaQUXx66yH6dB1V1sIjXafAA41DLALUKtd2H9Yj5J+BHqrrBPv7vWD/Wh7Adnf3D+Zp97RPY4sBqaYd+A3jOfiz+jn38MPCiHfC5oxDbHr7cBcCr9jTIGqw5ykL5LdZIcBPWfOIrkPv6RnofiujfpmGOLwKeFEth/7A9NTEfa0piEdCqqk8D/wf4vogMVwvrKawplixUdY+q/vMw12/Gmip4xXGsY2g6w+ZD5E4XGEqA0UM1GApARO4B/gZrSuBiVf22iPxYVVdnnLsNK0AUBf67c8QoIucAX1LV1S737WHgFvWvOq1hBIxDNRh8RERuBP6fHl+LOt72IsA1qnq/G+0ZxodxqAaDweASZg7VYDAYXMI4VIPBYHAJ41ANBoPBJYxDNRgMBpcwDtVgMBhcwjhUg8FgcAnjUA0Gg8El/j+cXdUY1b8iUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,4))\n",
    "ax = fig.add_subplot(111, title='Deviation power $P_{dev}$ (MW)',\n",
    "                          xlabel=P_mis_label, ylabel=E_sto_label)\n",
    "im = ax.imshow(pol_dev,  **im_opts)\n",
    "cbar = fig.colorbar(im, ticks=mpl.ticker.MultipleLocator())\n",
    "# contours\n",
    "clines = ax.contour(P_mis_grid, E_sto_grid, pol_dev,\n",
    "                [-1e-2, 1e-2],\n",
    "                colors='k', linestyles='solid', linewidths=.5)\n",
    "cbar.add_lines(clines)\n",
    "clines2 = ax.contour(P_mis_grid, E_sto_grid, pol_dev, [-2,-1,1,2],\n",
    "            colors='gray', linestyles='solid', linewidths=.5)\n",
    "cbar.add_lines(clines2, erase=False)\n",
    "\n",
    "# tolerance power\n",
    "clines_tol = ax.contour(P_mis_grid, E_sto_grid, pol_dev, [-P_tol, P_tol],\n",
    "            colors='r', linestyles='solid', linewidths=.5)\n",
    "cbar.add_lines(clines_tol, erase=False)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next steps:\n",
    "\n",
    "* simulate system trajectories using the optimized control, and\n",
    "* evaluate the control performance using *real data* (for production mismatch $P_{mis}$"
   ]
  }
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